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This work proposes a new perspective on the structure of Neural Networks(NNs). Traditional Neural Networks are typically tree-like structures forconvenience, which can be predefined or learned by NAS methods. However, such astructure can not facilitate communications between nodes at the same level orsignal transmissions to previous levels. These defects prevent effectivecollaboration, restricting the capabilities of neural networks. It iswell-acknowledged that the biological neural system contains billions of neuralunits. Their connections are far more complicated than the current NNstructure. To enhance the representational ability of neural networks, existingworks try to increase the depth of the neural network and introduce moreparameters. However, they all have limitations with constrained parameters. Inthis work, we introduce a synchronous graph-based structure to establish anovel way of organizing the neural units: the Neural Modules. This frameworkallows any nodes to communicate with each other and encourages neural units towork collectively, demonstrating a departure from the conventional constrainedparadigm. Such a structure also provides more candidates for the NAS methods.Furthermore, we also propose an elegant regularization method to organizeneural units into multiple independent, balanced neural modules systematically.This would be convenient for handling these neural modules in parallel.Compared to traditional NNs, our method unlocks the potential of NNs fromtree-like structures to general graphs and makes NNs be optimized in an almostcomplete set. Our approach proves adaptable to diverse tasks, offeringcompatibility across various scenarios. Quantitative experimental resultssubstantiate the potential of our structure, indicating the improvement of NNs.

Automatic Organization of Neural Modules for Enhanced Collaboration in Neural Networks